Taxonomies and knowledge graphs in Knowledge Engineering and Extended Entity Relationship (EER) diagrams in Software Engineering, are forms of conceptual models and the basis for the development of information systems. Verifying these conceptual models against their frame of reference is crucial to prevent defective model elements from entering the Software Development Life Cycle, as these models can be directly used to create software artefacts and source code, by means of Model-Driven Software Engineering techniques. Enhancing traditional Software Inspection practices with Human Computation and Crowdsourcing, Sabou, Winkler et. al proposed a generic approach to Verify Conceptual Models (VeriCoM). The performance of VeriCoM was tested with Crowdsourced Software Inspection (CSI) experiments on a Software Engineering use case, verifying the correctness of an EER model with respect to a textual system specication. These experiments followed a mostly manually performed scientic experiment-process, which was very time-consuming, error prone, not scalable and overall not applicable for a larger crowd of participants. Thus, the need for automation of this experiment-process through a software tool arose. The core work of this thesis consists of an analysis of the CSI-Experiment process and the detailed formal denition of the data model and algorithms needed to automate VeriCoM. These data models and algorithms are used to develop a CSI-Platform prototype which supports the experiments to validate VeriCoM. During a test under live conditions within a CSI-Experiment, the CSI-Platform greatly reduced the workload of the experiment administrations team and the subsequent evaluation questionnaire showed satised stakeholders. Therefore the formal denition of VeriCoM and the developed CSI-Platform can act as a starting point for the development of a full-edged software system to automate VeriCoM and other experiments within CSI.